Tag: interview

R Oracle Data Mining

Here is a new package called R ODM and it is an interface to do Data Mining via Oracle Tables through R. You can read more here http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html and here http://cran.fhcrc.org/web/packages/RODM/RODM.pdf . Also there is a contest for creative use of R and ODM.

R Interface to Oracle Data Mining

The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. R-ODM provides a powerful environment for prototyping data analysis and data mining methodologies.

R-ODM is especially useful for:

  • Quick prototyping of vertical or domain-based applications where the Oracle Database supports the application
  • Scripting of “production” data mining methodologies
  • Customizing graphics of ODM data mining results (examples: classification, regression, anomaly detection)

The R-ODM interface allows R users to mine data using Oracle Data Mining from the R programming environment. It consists of a set of function wrappers written in source R language that pass data and parameters from the R environment to the Oracle RDBMS enterprise edition as standard user PL/SQL queries via an ODBC interface. The R-ODM interface code is a thin layer of logic and SQL that calls through an ODBC interface. R-ODM does not use or expose any Oracle product code as it is completely an external interface and not part of any Oracle product. R-ODM is similar to the example scripts (e.g., the PL/SQL demo code) that illustrates the use of Oracle Data Mining, for example, how to create Data Mining models, pass arguments, retrieve results etc.

R-ODM is packaged as a standard R source package and is distributed freely as part of the R environment’s Comprehensive R Archive Network ( CRAN). For information about the R environment, R packages and CRAN, see www.r-project.org.

and

Present and win an Apple iPod Touch!
The BI, Warehousing and Analytics (BIWA) SIG is giving an Apple iPOD Touch to the best new presenter. Be part of the TechCast series and get a chance to win!

Consider highlighting a creative use of R and ODM.

BIWA invites all Oracle professionals (experts, end users, managers, DBAs, developers, data analysts, ISVs, partners, etc.) to submit abstracts for 45 minute technical webcasts to our Oracle BIWA (IOUG SIG) Community in our Wednesday TechCast series. Note that the contest is limited to new presenters to encourage fresh participation by the BIWA community.

Also an interview with Oracle Data Mining head, Charlie Berger https://decisionstats.wordpress.com/2009/09/02/oracle/

Business Analytics Analyst Relations /Ethics/White Papers

Curt Monash, whom I respect and have tried to interview (unsuccessfully) points out suitable ethical dilemmas and gray areas in Analyst Relations in Business Intelligence here at http://www.dbms2.com/2010/07/30/advice-for-some-non-clients/

If you dont know what Analyst Relations are, well it’s like credit rating agencies for BI software. Read Curt and his landscaping of the field here ( I am quoting a summary) at http://www.strategicmessaging.com/the-ethics-of-white-papers/2010/08/01/

Vendors typically pay for

  1. They want to connect with sales prospects.
  2. They want general endorsement from the analyst.
  3. They specifically want endorsement from the analyst for their marketing claims.
  4. They want the analyst to do a better job of explaining something than they think they could do themselves.
  5. They want to give the analyst some money to enhance the relationship,

Merv Adrian (I interviewed Merv here at http://www.dudeofdata.com/?p=2505) has responded well here at http://www.enterpriseirregulars.com/23040/white-paper-sponsorship-and-labeling/

None of the sites I checked clearly identify the work as having been sponsored in any way I found obvious in my (admittefly) quick scan. So this is an issue, but it’s not confined to Oracle.

My 2 cents (not being so well paid 😉 are-

I think Curt was calling out Oracle (which didnt respond) and not Merv ( whose subsequent blog post does much to clarify).

As a comparative new /younger blogger in this field,
I applaud both Curt to try and bell the cat ( or point out what everyone in AR winks at) and for Merv for standing by him.

In the long run, it would strengthen analyst relations as a channel if they separate financial payment of content from bias. An example is credit rating agencies who forgot to do so in BFSI and see what happened.

Customers invest millions of dollars in BI systems trusting marketing collateral/white papers/webinars/tests etc. Perhaps it’s time for an industry association for analysts so that individual analysts don’t knuckle down under vendor pressure.

It is easier for someone of Curt, Merv’s stature to declare editing policy and disclosures before they write a white paper.It is much harder for everyone else who is not so well established.

White papers can take as much as 25,000$ to produce- and I know people who in Business Analytics (as opposed to Business Intelligence) slog on cents per hour cranking books on R, SAS , webinars, trainings but there are almost no white papers in BA. Are there any analytics independent analysts who are not biased by R or SAS or SPSS or etc etc. I am not sure but this looks like a good line to  pursue 😉 – provided ethical checks and balances are established.

Personally I know of many so called analytics communities go all out to please their sponsors so bias in writing does exist (you cant praise SAS on a R Blogging Forum or R USers Meet and you cant write on WPS at SAS Community.org )

– at the same time someone once told me- It is tough to make a living as a writer, and that choice between easy money and credible writing needs to be respected.

Most sponsored white papers I read are pure advertisements, directed at CEOs rather than the techie community at large.

Almost every BI vendor claims to have the fastest database with 5X speed- and benchmarking in technical terms could be something they could do too.

Just like Gadget sites benchmark products, you can not benchmark BI or even BA products as it is written not to do so  in many licensing terms.

Probably that is the reason Billions are spent in BI and the positive claims are doubtful ( except by the sellers). Similarly in Analytics, many vendors would have difficulty justifying their claims or prices if they are subjected to a side by side comparison. Unfortunately the resulting confusion results in shoddy technology coming stronger due to more aggressive marketing.

Protected: Analyzing SAS Institute-WPS Lawsuit

This content is password-protected. To view it, please enter the password below.

Interview Terri Rylander Advanced Marketing Collateral BI

Here is an interview with the fabulous Terri Rylander, innovative and creative Business Intelligence marketing consultant and the principal of Advanced Marketing Collateral . As the BI marketing wars heat up, cost pressures on optimize marketing ROI and emerging marketing channels will lead to a trend in which BI vendors would choose the best resources not just the in-house resources. Marketing communication remain the un-sung heroes of Business Intelligence with all the glamour and focus on the techies- who surprisingly are now building more and more similar algorthms. Design in user interfaces and creativity in marketing could be a new tool in marketing Business Intelligence.

Ajay- Explain briefly what it is you do in the business intelligence space.
Terri- I am a freelance writer creating marketing material for BI vendors. I create case studies, white papers, brochures, articles, web content and short copy like e-mails and postcards. Because I’m still a techie girl at heart, I also design and develop websites including WordPress customization which is quite popular now.

Ajay- How did you come to specialize in marketing for BI?
Terri- In the late ‘90s I went to work as a web developer for a large telecommunication company. In just a few years, I began managing the development group. Then I was asked to come to a young wireless company and manage their reporting group. After building out a solid enterprise reporting system (that still stands today), I went after a more holistic approach to reporting and analysis and created one of the first BICCs (business intelligence competency centers) in the country. Our group managed the oversight of the entire BI program including strategy, training, data quality, end user support, and BI communications. After some shifting in the winds, I knew it was time to move on.
In looking for that next “thing” I did a fair amount of soul searching. I knew I wanted a career that was both flexible and portable. I thought, “Why not use my experience in BI to create the very things I had consumed as a customer?” That’s when Advanced Marketing Collateral was born.

Ajay- How do you see the BI market place changing?
Terri- I guess that’s part of why I’ve been drawn to the BI field all these years. It just continues to change and improve. Just when you think they’ve done it all, a totally new concept emerges. However, I don’t envy the vendors. The competition is always at your heels. It seems like all the vendors have a solution for every industry or business line (though not so much for the emerging corporate sustainability area). Vendors have to continually experiment with new features and directions. Some will succeed and others will fail. It’s increasingly important and now even easier to communicate with both potential and existing customers, letting them know where you’re going and why, getting feedback, and just getting them to know more about the personality of your company. This can create such a strong bond.

Ajay- So how has that changed the way vendors should market their BI products?
Terri- It used to be you took out ads in the various BI publications, published and sent 12 page white papers, put up a website with technical descriptions for your products, and sent your sales force off to do “dog-and-pony” shows at prospective customer sites. Most of that is still valid, but Continue reading “Interview Terri Rylander Advanced Marketing Collateral BI”

Interview Eric A. King President The Modeling Agency

Here is an interview with Eric King, President, The Modeling Agency.

eric-king

Ajay- Describe your career journey. What interested you in science? How do you think we can help more young people get interested in science?

Eric- I was a classic underachiever in school. I was bright, but was generally disinterested in academics, and focused on… well, other things at the time. However, I had always excelled in math and science, and actually paid attention those classes.

I was a high school junior when my school acquired its first computers: Apple IIs. There were no formal computer courses, so instead of study hall, I would go to the lab and tinker. Sure, I would join a few other geeks (well before it was cool to be such) for a few primitive games, but would spend the majority of my time reading about the Basic programming language and coding graphic designs, math formulas and simple games.

I loved it so much that I had decided to pursue computer science as a college major before my senior year and it went into my yearbook entry. Fortunately, my relatively high SAT scores offset my poor high school GPA and squeaked me into the University of Pittsburgh’s trial-by-fire summer program. It was the first time I really felt I had to perform (or else) and had to work hard to overcome poor study habits — but rose to the occasion with room to spare.

I’m glad I did not realize at the time that Pitt was #9 in the nation for computer science. I did have a hint though when I realized the extremely high attrition rate. In the end, our freshman class of 240 graduated 36. I did make it through the freshman year that trimmed the first half of the original group, but was a casualty my sophomore year when I fell short of a passing grade in a core CS course that was only offered annually. I repeated it the following year and graduated with extra credits – to include a directed study in table tennis (no kidding).

I loved the programming assignments but loathed the tests. After slogging through the program and graduating, I took a three month break. I figured it would be my last opportunity to be free of responsibility for that period of time possibly until retirement – and so far, I’m right.

Then, my cousin who graduated with me told me about a neural computing software tools company in Pittsburgh, called NeuralWare. I was always intrigued by “artificial intelligence”, but they were seeking a technical support representative. I realized my junior year that I did not want to code or remain on the technical side for a living, but go into business development, project management, business management and entrepreneurship. Yet, after having survived the majority of the attrition, I did want to complete my technical degree, then seek the business angle.

A short while later, NeuralWare contacted me again to start up their sales operation (a role previously fulfilled a co-founder). This was the start I was seeking: cut my teeth in business for highly technical products. I participated in numerous training sessions for neural computing and related technologies and loved it. The notion that the computer could leverage mathematics that emulated the basic learning function of the brain, or treat a formula like a gene – split it, mutate it, test and progress toward the most fitting solution was beyond exciting to me. So much so, that I’ve not left the technology in the 19 years since.

Drawing others to science, I believe is more a matter of nature over nurture. I am the father of twin boys who couldn’t have greater differences in interests, personalities and talents. In that spirit, I believe that science should be made readily available, involve both theory and practice, and be presented in a manner that motivates those who are drawn to science to excel. But I don’t believe science can be effectively pushed to those whose inherit interests and passion lie elsewhere (reference the character Neil Perry in The Dead Poet’s Society).

Ajay- Describe the path that The Modeling Agency has traveled. What is your vision for it for the future.

Eric- The Modeling Agency (TMA) was established as a highly structured formal network of senior-level consultants in January of 2000. TMA’s initial vision (and sustained slogan) was to “provide guidance and results to those who are data-rich, yet information-poor.” I still have not encountered an organization that holds a larger bench of senior-level data mining consultants and trainers. And to be senior-level, TMA consultants must be far more than technically steeped in data mining. TMA’s senior consulting staff are business consultants first – not rushing to analyze data, but assessing an organization’s environment and designing a fitting solution to resources that support stated objectives.

There are three primary divisions to TMA: training, consulting and solutions. Each division is part of an overarching business and technology maturation process. For example, training generates technology advocates for data mining that encourages consulting engagements which at times lead to productizable vertical market services that create solutions which allow other organizations to capitalize on the risk that pioneering organizations had undertaken, and springboard on the return realized by implementations within their vertical – which leads to new discoveries and innovations that feed back to training.

Beyond further developing the brand of TMA’s quickly emerging niche (described later), our future vision involves developing two specific types of vendor partnerships to allow TMA to redirect the substantial margins enjoyed by its clients through the application of predictive modeling into a residual stream of income to accelerate the growth of TMA itself. While this operation is confidential, we will be pleased to tell our future clients that we do indeed apply our services for the benefit of our own business.

Ajay- Describe the challenges and opportunities in modeling through recent innovations. i.e social network analysis software and increasing amounts of customer text data available on social media.

Eric- Please allow me to shift the focus of this question slightly. So many organizations are still making their way down the Business Intelligence chain to applying predictive modeling on standard operational data, that social network analysis and customer text analytics remains more of a research endeavor in my opinion. As a practical applications company, TMA focuses its experience in pragmatically applying its business problem solving creativity on operational and transactional data enriched by demographic and psychographic attributes. I feel that the areas of social media and social network analysis are not yet mature enough to be formalized as established practice on TMA’s menu of service offerings.

Having said that, the greatest challenges in predictive modeling are no longer in applying the methodological tactics, but rather in the comprehensive assessment, strategic problem design, project definition, results interpretation and ROI calculation. Popular data mining software is now highly effective at automating the tactical model building process – many packages running numerous methods in parallel and selecting the best performer.

So, the challenges that remain today are in tackling the tails of the process as mentioned above. This is where TMA’s expertise is focused and where our niche is quickly emerging: guiding organizations to establish their own internal predictive analytics operation.

Ajay- In the increasing game of consolidation of business intelligence vendors and data mining and analytics, which are the vendors that you have worked with and what are their relative merits.

Eric- TMA has established formal partnerships with several popular data mining tool vendors and services companies. Despite these alliances, TMA remains vendor neutral and method agnostic for clients that approach TMA directly. Having said that, I will make a general statement that there is notable merit for the organizations that recognize that they must ensure their client’s success in the full implementation cycle of data mining – not just provide a great tool that addresses the center.

In fact, it was one of TMA’s earliest partners who saw the value in teaming with TMA to support the ends of the data mining process (assessment, business understanding project definition and design, results interpretation, implementation) while their solution addressed the middle (data preparation and modeling). They recognized that as great as their tool was, it was still hitting the shelf soon after the sale. The realized that their clients were building very good models that answered the wrong questions, or were uninterpretable and incapable of implementation.

TMA soon recognized that these excellent tools combined with TMA’s strategic data mining mentorship and counsel provided the capability for organizations to essentially establish their own internal predictive analytic practice with existing business practitioners – not requiring senior statisticians or PhDs. This has become a popular and fast growing service, for which TMA’s large bench of senior-level data mining consultants is perfectly suited to fulfill.

And the best candidates for this service are those organizations who have attempted pilots or projects but fell short of their objectives. And while the acquisition of SPSS (who licenses a reputable predictive analytics tool, “PASW”) by IBM (the gold standard for IT and BI services and solutions) may be the closest competition that TMA may encounter, TMA enjoys a substantial head start and foothold with its numerous formal alliances, vendor neutrality and sizable client list specific to predictive modeling. TMA is quickly becoming the standard to turn to for progressive organizations that realize internalizing predictive analytics is not just a matter of when rather than whether, but that it is within their grasp with TMA’s guidance and the right tool(s).

Ajay- What do people at The Modeling Agency do for fun?

Eric- Our interests are as diverse as we are geographically disbursed. One of our senior consultants is a talented and fairly established tango dancer. He’s always willing to travel for assignments, as he’s anxious to tap into that city’s tango circuit. Another consultant is an avid runner, entering marathons and charity races. One common thread that most of us share is our dedication to parenting. We all love trips and time with our children. In fact, I’m writing this on a return trip from Disney World on the Auto Train with my 5 year old twin boys – a trip I know I’ll recall fondly through my remaining years.

Bio

Eric A. King is President and Founder of The Modeling Agency (TMA), a US-based company started in January 2000 that provides training, consulting, solutions and a popular introductory webinar in predictive modeling “for those who are data-rich, yet information-poor.”  King holds a BS in computer science from the University of Pittsburgh and has over 19 years of experience specifically in data mining, business development and project management.  Prior to TMA, King worked for NeuralWare, a neural network tools company, and American Heuristics Corporation, an artificial intelligence consulting firm.  He may be reached at eric@the-modeling-agency.com or (281) 667-4200 x210.

Interview Gregory Piatetsky KDNuggets.com

Here is an interviw with Gregory Piatetsky, founder and editor of KDNuggets (www.KDnuggets.com ) ,the oldest and biggest independent industry websites in terms of data mining and analytics-

gps6

Ajay- Please describe your career in science, many challenges and rewards that came with it. Name any scientific research, degrees teaching etc.


Gregory- I was born in Moscow, Russia and went to a top math high-school in Moscow. A unique  challenge for me was that my father was one of leading mathematicians in Soviet Union.  While I liked math (and still do), I quickly realized while still in high school that  I will never be as good as my father, and math career was not for me.

Fortunately, I discovered computers and really liked the process of programming and solving applied problems.  At that time (late 1970s) computers were not very popular and it was not clear that one can make a career in computers.  However I was very lucky that I was able to pursue what I liked and find demand for my skills.

I got my MS in 1979 and PhD in 1984 in Computer Science from New York University.
I was interested in AI (perhaps thanks to a lot of science fiction I read as a kid), but found a job in databases, so I was looking for ways to combine them.

In 1984 I joined GTE Labs where I worked on research in databases and AI, and in 1989 started the first project on Knowledge Discovery in data. To help convince my management that there will be a demand for this thing
called “data mining” (GTE management did not see much future for it), I also organized a AAAI workshop on the topic.

I thought “data mining” is not sexy enough name, and so I called it “Knowledge Discovery in Data”, or KDD.  Since 1989, I was working on KDD and data mining in all aspects – more on my page www.kdnuggets.com/gps.html

Ajay-  How would you encourage a young science entrepreneur in this recession.

Gregory- Many great companies were started or grew in a recession, e.g.
http://www.insidecrm.com/features/businesses-started-slump-111108/

Recession may be compared to a brush fire which removes dead wood and allows new trees to grow.

Ajay- What prompted you to set up KD Nuggets? Any reasons for the name (kNowledge Discovery Nuggets). Describe some key milestones in this iconic website for data mining people.

Gregory- After a third KDD workshop in 1993 I started a newsletter to connect about 50 people who attended the workshop and possibly others who were interested in data mining and KDD.  The idea was that it will have short items or “nuggets” of information. Also, at that time a popular metaphor for data miner was gold miners who were looking for gold “nuggets”.  So, I wanted a newsletter with “nuggets” – short, valuable items about Knowledge Discovery.  Thus, the name KDnuggets.

In 1994 I created a website on data mining at GTE and in 1997, after I left  GTE , I moved it to the current domain name www.kdnuggets.com .

In 1999, I was working for startup which provided data mining services to financial industry.  However, because of Y2K issues, all banks etc froze their systems in the second half of 1999, and we had very little work (and our salaries were reduced as well).  I decided that I will try to get some ads and was able to get companies like SPSS and Megaputer to advertise.

Since 2001, I am an independent consultant and KDnuggets is only part of what I am doing.  I also do data mining consulting, and actively participate in SIGKDD (Director 1998-2005, Chair 2005-2009).

Some people think that KDnuggets is a large company, with publisher, webmaster, editor, ad salesperson, billing dept, etc.  KDnuggets indeed has all this functions, but it is all me and my two cats.

Ajay- I am impressed by the fact KD nuggets is almost a dictionary or encyclopedia for data mining. But apart from advertising you have not been totally commercial- many features of your newsletter remain ad free – you still maintain a minimalistic look and do not take sponsership aligned with one big vendor. What is your vision for KD Nuggets for the years to come to keep it truly independent.

Gregory- My vision for KDnuggets is to be a comprehensive resource for data mining community, and I really enjoyed maintaining such resource for the first 7-8 years completely non-commercially. However, when I became self -employed, I could not do KDnuggets without any income, so I selectively introduced ads, and only those which are relevant to data mining.

I like to think of KDnuggets as a Craiglist for data mining community.

I certainly realize the importance of social media and Web 2.0 (and interested people can follow my tweets at tweeter.com/kdnuggets)  and plan to add more social features to KDnuggets.

Still, just like Wikipedia and Facebook do not make New York Times obsolete, I think there is room and need for an edited website, especially for such a nerdy and not very social group like data miners.

Ajay- What is the worst mistake/error in writing publishing that you did. What is the biggest triumph or high moment in the Nuggets history.

Gregory- My biggest mistake is probably in choosing the name kdnuggets – in retrospect,  I could have used a shorter and easier to spell domain name, but in 1997 I never expected that I will still be publishing www.KDnuggets.com 12 years later.

Ajay- Who are your favourite data mining students ( having known so many people). What qualities do you think set a data mining person apart from other sceinces.

Gregory- I was only an adjunct professor for a short time, so I did not really have data mining students, but I was privileged enough to know many current data mining leaders when they were students.  Among more recent students, I am very impressed with Jure Leskovec, who just finished his PhD and got the best KDD dissertation award.

Ajay- What does Gregory Piatetsky do for fun when he is not informing the world on analytics and knowledge discovery.

Gregory- I enjoy travelling with my family, and in the summer I like biking and windsurfing.
I also read a lot, and currently in the middle of reading Proust (which I periodically dilute by other, lighter books).

Ajay- What is your favourite reading blog and website ? Any India plans to visit.
Gregory– I visit many blogs on www.kdnuggets.com/websites/blogs.html

and I like especially
– Matthew Hurst blog: Data Mining: Text Mining, Visualization, and Social Media
– Occam’s Razor by Avinash Kaushik, examining web analytics.
– Juice Analytics, blogging about analytics and visualization
– Geeking with Greg, exploring the future of personalized information.

I also like your website decisionstats.com and plan to visit it more frequently

I visited many countries, but not yet India – waiting for the right occasion !

Biography–

(http://www.kdnuggets.com/gps.html)

Gregory Piatetsky-Shapiro, Ph.D. is the President of KDnuggets, which provides research and consulting services in the areas of data mining, web mining, and business analytics. Gregory is considered to be one of the founders of the data mining and knowledge discovery field.Gregory edited or co-edited many collections on data mining and knowledge discovery, including two best-selling books: Knowledge Discovery in Databases (AAAI/MIT Press, 1991) and Advances in Knowledge Discovery in Databases (AAAI/MIT Press, 1996), and has over 60 publications in the areas of data mining, artificial intelligence and database research.

Gregory is the founder of Knowledge Discovery in Database (KDD) conference series. He organized and chaired the first three Knowledge Discovery in Databases (KDD) workshops in 1989, 1991, and 1993. He then served as the Chair of KDD Steering committee and guided the conversion of KDD workshops into leading international conferences on data mining. He also was the General Chair of the KDD-98 conference.

Interview Tasso Argyros CTO Aster Data Systems

Here is an interview with Tasso Argyros,the CTO and co-founder of Aster Data Systems (www.asterdata.com ) .Aster Data Systems is one of the first DBMS to tightly integrate SQL with MapReduce.

tassos_argyros

Ajay- Maths and Science students the world over are facing a major decline. What would you recommend to young students to get careers in science.

[TA] –My father is a professor of Mathematics and I spent a lot of my college time studying advanced math. What I would say to new students is that Math is not a way to get  a job, it’s a way to learn how to think. As such, a Math education can lead to success in any discipline that requires intellectual abilities. As long as they take the time to specialize at some point – via  postgraduate education or a job where they can learn a new discipline from smart people – they won’t regret the investment.

Ajay- Describe your career in Science particularly your time at Stanford. What made you think of starting up Asterdata. How important is it for a team rather than an individual to begin startups. Could you describe the startup moment when your team came together.

[TA] – While at Stanford I became very familiar with the world of startups through my advisor, David Cheriton (who was an angel investor in VMWare, Google and founder of two successful companies). My research was about processing large amounts of data on large, low-cost computer farms. A year into my research it became obvious that this approach had huge processingpower advantages and it was superior to anything else I could see in the marketplace. I then happened to meet my other two co-founders, Mayank Bawa & George Candea who were looking at a similar technical problem from the database and reliability perspective, respectively.

I distinctly remember George walking into my office one day (I barely knew him back then) and saying “I want talk to you about startups and the future” – the rest has become history.

Ajay- How would you describe your product Aster nCluster Cloud Edition to omebody who does not anything beyond the Traditional Server/ Datawarehouse technologies. Could you rate it against some known vendors and give a price point specific to what level of usage does the Total Cost of Ownership in Asterdata becomes cheaper than a say Oracle or a SAP or a Microsoft Datawarehosuing solution.

[TA]- Aster allows businesses  to reduce the data analytics TCO in two interesting ways. First, it has a much lower hardware cost than any traditional DW technology because of its use of commodity servers or cloud infrastructure like Amazon EC2. Secondly, Aster has implemented a lot of  innovations that simplify the (previously tedious and expensive) management of the system, which includes scaling the system elastically up/down as needed – so they are not paying for capacity they don’t need at a given point in time.

But cutting costs is one side of the equation; what makes me even more excited is the ability to make a business more profitable, competitive and efficient through analyzing more data at greaterdepth. We have customers that have cut their costs and increased their customers and revenue by using Aster to analyze their valuable (and usually underutilized) data. If you have data – and you think you’re not taking full advantage of it – Aster can help.

Ajay- I have always have this one favourite question.When can I analyze 100 giga bytes of data using just a browser and some statistical software like R or advanced forecasting softwares that are available.Describe some of Asterdata ‘s work in enhancing the analytical capabilities of big data.

Can I run R ( free -open source) on an on demand basis for an Asterdata solution. How much would it cost me to crunch 100 gb of data and make segmentations and models with say 50 hours of processing time per month

[TA]- One of the big innovations that Aster does it to allow analytical applications like R to be embedded in the database via our SQL/MapReduce framework. We actually have customers right now that are using R to do advanced analytics over terabytes of data.  100GB is actually on the lower end of what our software can enable and as such the cost would not be significant.

Ajay- What do people at Asterdata do when not making complex software.

[TA]- A lot of Asterites love to travel around the world – we are, after all, a very diverse company. We also love coffee, Indian food as well as international and US sports like soccer, cricket, cycling,and football!

Ajay- Name some competing products to Asterdata and where Asterdata products are more suitable for a TCO viewpoint. Name specific areas where you would not recommend your own products.

[TA]- We go against products like Orace database, Teradata and IBM DB2. If you need to do analytics over 100s of GBs or terabytes of data, our price/performance ratio would be orders of magnitude better.

Ajay- How do you convince named and experienced VC’s Sequia Capital to invest in a start-up ( eg I could do with some server costs coming financing)

[TA]- You need to convince Sequoia of three things. (a) that the market you’re going after is very large (in the billions of dollars, if you’re successful). (b) that your team is the best set of people that could ever come together to solve the particular problem you’re trying to solve. And (c) that the technology you’ve developed gives you an “unfair advantage” over incumbents or new market entrants.  Most importantly, you have to smile a lot! J

Biography

About Tasso:

Tasso (Tassos) Argyros is the CTO and co-founder of Aster Data Systems, where he is responsible for all product and engineering operations of the company. Tasso was recently recognized as one ofBusinessWeek’s Best Young Tech Entrepreneurs for 2009 and was an SAP fellow at the Stanford Computer Science department. Prior to Aster, Tasso was pursuing a Ph.D. in the Stanford Distributed Systems Group with a focus on designing cluster architectures for fast, parallel data processing using large farms of commodity servers. He holds an MsC in Computer Science from Stanford University and a Diploma in Computer and Electrical Engineering from Technical University of Athens.

About Aster:

Aster Data Systems is a proven leader in high-performance database systems for data warehousing and analytics – the first DBMS to tightly integrate SQL with MapReduce – providing deep insights on data analyzed on clusters of low-cost commodity hardware.

The Aster nCluster database cost-effectively powers frontline analytic applications for companies such as MySpace, aCerno (an Akamai company), and ShareThis. Running on low-cost off-the-shelf hardware, and providing ‘hands-free’ administration, Aster enables enterprises to meet their data warehousing needs within their budget.

Aster is headquartered in San Carlos, California and is backed by Sequoia Capital, JAFCO Ventures, IVP, Cambrian Ventures, and First-Round Capital, as well as industry visionaries including David Cheriton, Rajeev Motwani and Ron Conway.

Aster_logo_3.0_red

Related Articles